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Pythagorean fuzzy incidence graphs with application in one-way toll road network

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Abstract

Incidence graphs are an effective to model interconnected networks with additional vertex-edge interactions. They are widely used to establish modes of operation and controllers to illustrate the influence of one factor on another. The purpose of this paper is to present the concept of directed Pythagorean fuzzy incidence graphs. Physical problems that Pythagorean fuzzy incidence graphs cannot effectively illustrate can be modeled by restricting the flow direction because the interactions in these graphs are not symmetric. We discuss the connectivity of directed Pythagorean fuzzy incidence graphs differently by emphasizing legal and illegal network flows. Furthermore, we introduce the concept of legal and illegal flow reduction vertices, edges, and pairs in directed Pythagorean fuzzy incidence graphs and give some significant results. We study the types of legal edges in directed Pythagorean fuzzy incidence graphs and establish some results about legal strong edges. Moreover, we discuss the application of legal fuzzy incidence trees in decision-making, that is, to select the optimal location of the electronic toll collection system on one-way toll roads to maximize toll revenue. Additionally, we provide an algorithm to understand the methods we use in our application. Finally, we compare the proposed method with an existing model that includes numerical results and logical arguments to demonstrate its feasibility and applicability.

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References

  • Akram M, Ahmad U, Al-Shamiri MMA, Shareef A (2024) Algorithms for computing Pythagorean fuzzy average edge connectivity of Pythagorean fuzzy graphs. J Appl Math Comput. https://doi.org/10.1007/s12190-023-01970-9

    Article  MathSciNet  Google Scholar 

  • Akram M, Naz S (2018) Energy of Pythagorean fuzzy graphs with applications. Mathematics 6(8):136. https://doi.org/10.3390/math6080136

    Article  Google Scholar 

  • Akram M, Dar JM, Farooq A (2018) Planar graphs under Pythagorean fuzzy environment. Mathematics 6(12):278. https://doi.org/10.3390/math6120278

    Article  Google Scholar 

  • Akram M, Habib A, Davvaz B (2019) Direct sum of \(n\) Pythagorean fuzzy graphs with application to group decision-making. J Multiple-Valued Logic Soft Comput 33(1–2):75–115

    MathSciNet  Google Scholar 

  • Akram M, Habib A, Ilyas F, Dar JM (2018) Specific types of Pythagorean fuzzy graphs and application to decision-making. Math Comput Appl 23(3):42. https://doi.org/10.3390/mca23030042

    Article  MathSciNet  Google Scholar 

  • Akram M (2011) Bipolar fuzzy graphs. Inform Sci 181:5548–5564

    Article  MathSciNet  Google Scholar 

  • Akram M, Dudek WA (2011) Interval-valued fuzzy graphs. Comput Math Appl 61:289–299

    Article  MathSciNet  Google Scholar 

  • Akram M, Habib A, Allahviranloo A (2022) A new maximal flow algorithm for solving optimization problems with linguistic capacities and flows. Inform Sci 612:201–230

    Article  Google Scholar 

  • Akram M, Ashraf A (2014) Sarwar M (2014) Novel applications of intuitionistic fuzzy digraphs in decision support systems. Sci World J 3:904606

    Google Scholar 

  • Akram M, Yousuf M, Allahviranloo T (2023) Solution of the Pythagorean fuzzy wave equation with Pythagorean fuzzy Fourier sine transform. Granular Comput 8:1149–1171. https://doi.org/10.1007/s41066-023-00400-2

    Article  Google Scholar 

  • Akram M, Yousuf M, Allahviranloo T (2024) An analytical study of Pythagorean fuzzy fractional wave equation using multivariate Pythagorean fuzzy fourier transform under generalized Hukuhara Caputo fractional differentiability. Granular Computing, 9(15). https://doi.org/10.1007/s41066-023-00440-8

  • Atanassov KT (1983) Intuitionistic fuzzy sets. VII ITKRs Session, Sofia, Deposed in Central Science Technology Library of Bulgarian Academy of Science 1697/84

  • Atanassov KT (1999) Intuitionistic fuzzy sets: theory and applications. Physica-Verlag, Heidelberg, pp 1–137

    Google Scholar 

  • Bera J, Das KC, Samanta S, Lee JG (2023) Connectivity status of intuitionistic fuzzy graph and its application to merging of banks. Mathematics 11(8):1949. https://doi.org/10.3390/math11081949

    Article  Google Scholar 

  • Bhattacharya P (1987) Some remarks on fuzzy graphs. Pattern Recognit Lett 6(5):297–302

    Article  ADS  Google Scholar 

  • Bhutani KR, Rosenfeld A (2003) Strong arcs in fuzzy graphs. Inform Sci 152:319–322

    Article  MathSciNet  Google Scholar 

  • Brualdi RA, Massey JJQ (1993) Incidence and strong edge colorings of graphs. Discrete Math 122(1–3):51–58

    Article  MathSciNet  Google Scholar 

  • Chakraborty D, Mahapatra NK (2020) Notes on intuitionistic fuzzy graph. Int J Adv Math 1:9–23

    Google Scholar 

  • Chen SM and Wang NY (2010) Fuzzy Forecasting Based on Fuzzy-Trend Logical Relationship Groups. In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(5):1343-1358

  • Chen SM, Ko YK, Chang YC, Pan JS (2009) Weighted Fuzzy Interpolative Reasoning Based on Weighted Increment Transformation and Weighted Ratio Transformation Techniques. In: IEEE Transactions on Fuzzy Systems, 17(6):1412-1427

  • Chen SM, Jian WS (2017) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques. Inform Sci 391–392:65–79

    Article  Google Scholar 

  • Chen SM, Zou XY, Gunawan GC (2019) Fuzzy time series forecasting based on proportions of intervals and particle swarm optimization techniques. Inform Sci 500:127–139

    Article  MathSciNet  Google Scholar 

  • Chen SM, Wang JY (1995) Document retrieval using knowledge-based fuzzy information retrieval techniques. In: IEEE Transactions on Systems, Man, and Cybernetics, 25(5):793-803

  • Dinesh T (2012) A study on graph structures, incidence algebras and their fuzzy analogues. Ph.D. Thesis, Kannur University, Kerala, India

  • Dinesh T (2016) Fuzzy incidence graph - An introduction. Adv Fuzzy Sets Syst 21(1):33–48

    Article  MathSciNet  Google Scholar 

  • Fang J, Nazeer I, Rashid T, Liu JB (2021) Connectivity and Wiener index of fuzzy incidence graphs. Math Prob Eng 2021:6682966. https://doi.org/10.1155/2021/6682966

    Article  MathSciNet  Google Scholar 

  • Gayathri G, Mathew S, Mordeson JN (2022) Directed fuzzy incidence: a model for illicit flow networks. Inform Sci 608:1375–1400

    Article  Google Scholar 

  • Gayathri G, Mathew S, Mordeson JN (2023) Max-flow min-cut theorem for directed fuzzy incidence networks. J Appl Math Comput 1–25. https://doi.org/10.1007/s12190-023-01952-x

  • Gayathri G, Mathew S, Mordeson JN (2023) Connectivity of directed fuzzy incidence graphs applied to traffic networks. J Appl Math Comput 69:3317–3336

    Article  MathSciNet  Google Scholar 

  • Habib A, Akram M, Kahraman (2022) Minimum spanning tree hierarchical clustering algorithm: a new Pythagorean fuzzy similarity measure for the analysis of functional brain networks. Expert Syst Appl 201:117016

    Article  Google Scholar 

  • Horng YJ, Chen SM, Chang YC, Lee CH (2005) A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. In: IEEE Transactions on Fuzzy Systems, 13(2):216-228

  • Ibrahim HZ (2024) Multi-criteria decision-making based on similarity measures on interval-valued bipolar n,m-rung orthopair fuzzy sets. Granular Computing, 9(5). https://doi.org/10.1007/s41066-023-00429-3

  • Kaufmann A (1973) Introduction a la Theorie des Sour-Ensembles Flous. Masson et Cie, Paris, France

    Google Scholar 

  • Luqman A, Shahzadi G (2023) Multi-criteria group decision-making based on the interval-valued q-rung orthopair fuzzy SIR approach for green supply chain evaluation and selection. Granular Comput 8:1937–1954

    Article  Google Scholar 

  • Malik DS, Mathew S, Mordeson JN (2018) Fuzzy incidence graphs: applications to human trafficking. Inform Sci 447:244–255

    Article  Google Scholar 

  • Mathew S, Sunitha M (2009) Types of arcs in a fuzzy graph. Inform Sci 179(11):1760–1768

    Article  MathSciNet  Google Scholar 

  • Mathew S, Sunitha M (2010) Node connectivity and arc connectivity in fuzzy graphs. Inform Sci 180(4):519–531

    Article  MathSciNet  Google Scholar 

  • Mathew S, Mordeson JN (2017) Connectivity concepts in fuzzy incidence graphs. Inform Sci 382–383:326–333

    Article  MathSciNet  Google Scholar 

  • Mordeson JN, Mathew S (2017) Fuzzy end nodes in fuzzy incidence graphs. New Math Nat Comput 13(1):13–20

    Article  MathSciNet  Google Scholar 

  • Mathew S, Mordeson JN, Yang HL (2019) Incidence cuts and connectivity in fuzzy incidence graphs. Iranian J Fuzzy Syst 16(2):31–43

    MathSciNet  Google Scholar 

  • Mordeson JN (2016) Fuzzy incidence graphs. Adv Fuzzy Sets Syst 21(2):121–131

    Article  Google Scholar 

  • Nazeer I, Rashid T, Hussain MT (2021) Cyclic connectivity index of fuzzy incidence graphs with applications in the highway system of different cities to minimize road accidents and in a network of different computers. PLOS One, 16(9). http://dx.doi.org/10.1371/journal.pone.0257642

  • Nazeer I, Rashid T (2022) Connectivity concepts in intuitionistic fuzzy incidence graphs with application. Int J Appl Comput Math 8(263). https://doi.org/10.1007/s40819-022-01461-8

  • Nazeer I, Rashid T, Keikha A (2021) An application of product of intuitionistic fuzzy incidence graphs in textile industry. Complexity 2021:1–16

    Article  Google Scholar 

  • Naz S, Ashraf S, Akram M (2018) A novel approach to decision-making with Pythagorean fuzzy information. Mathematics 6(6):95. https://doi.org/10.3390/math6060095

    Article  Google Scholar 

  • Rosenfeld A (1975) Fuzzy graphs. Fuzzy sets and their applications to cognitive and decision processes. Academic press, New York, pp 77–95

    Chapter  Google Scholar 

  • Sarwar M, Akram M, Shahzadi S (2021) Bipolar fuzzy soft information applied to hypergraphs. Soft Comput 25:3417–3439

    Article  Google Scholar 

  • Sarwar S, Akram M (2017) Novel concepts of bipolar fuzzy competition graphs. J Appl Math Comput 54:511–547

    Article  MathSciNet  Google Scholar 

  • Shahzadi S, Rasool A, Sarwar M, Akram M (2021) A framework of decision making based on bipolar fuzzy competition hypergraphs. J Intell Fuzzy Syst 41(1):1319–1339

    Article  Google Scholar 

  • Shareef A, Ahmad U, Saddique S, Al-Shamiri MMA (2023) Pythagorean fuzzy incidence graphs with application in illegal wildlife trade. AIMS Math 8(9):21793–21827

    Article  Google Scholar 

  • Wu SY (1986) The compositions of fuzzy digraphs. J Res Educ Sci 31:603–628

    ADS  Google Scholar 

  • Yager RR (2013) Pythagorean fuzzy subsets. Joint IFS World Congress and NAFIPS Annual Meeting. Edmonton, Canada, pp 57–61

    Chapter  Google Scholar 

  • Yager RR, Abbasov AM (2013) Pythagorean membership grades, complex numbers, and decision-making. Int J Intell Syst 28(5):436–452

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inform Control 8(3):338–353

    Article  Google Scholar 

  • Zf Zhang, Yao B, Jw Li, Lz Linu, Wang Jf, Bg Xu (2008) On Incidence Graphs. ARS combinatoria 87:213–223

    MathSciNet  Google Scholar 

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MA, AS, ANA-K: concept, design, analysis, writing, or revision of the manuscript.

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Correspondence to Muhammad Akram.

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Akram, M., Shareef, A. & Al-Kenani, A.N. Pythagorean fuzzy incidence graphs with application in one-way toll road network. Granul. Comput. 9, 39 (2024). https://doi.org/10.1007/s41066-024-00455-9

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